MIRL: Multiple Imputation Random Lasso for Variable Selection with Missing Entries

Implements a variable selection and prediction method for high-dimensional data with missing entries following the paper Liu et al. (2016) <doi:10.1214/15-AOAS899>. It deals with missingness by multiple imputation and produces a selection probability for each variable following stability selection. The user can further choose a threshold for the selection probability to select a final set of variables. The threshold can be picked by cross validation or the user can define a practical threshold for selection probability. If you find this work useful for your application, please cite the method paper.

Version: 1.0
Depends: glmnet, mice, MASS, boot
Published: 2018-04-11
Author: Ying Liu, Yuanjia Wang, Yang Feng, Melanie M. Wall
Maintainer: Ying Liu <summeryingl at gmail.com>
License: GPL-2
NeedsCompilation: no
CRAN checks: MIRL results

Downloads:

Reference manual: MIRL.pdf
Package source: MIRL_1.0.tar.gz
Windows binaries: r-devel: MIRL_1.0.zip, r-release: MIRL_1.0.zip, r-oldrel: MIRL_1.0.zip
OS X binaries: r-release: MIRL_1.0.tgz, r-oldrel: not available

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